Characterization of disease-related covariance topographies with SSMPCA toolbox: effects of spatial normalization and PET scanners.
Characterization of disease-related covariance topographies with SSMPCA toolbox: effects of spatial normalization and PET scanners.
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DOI：
10.1002/hbm.22295
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发表时间：
2014-05
影响因子：
4.8
通讯作者：
Eidelberg, David
中科院分区：
文献类型：
--
作者：
Peng, Shichun;Ma, Yilong;Spetsieris, Phoebe G.;Mattis, Paul;Feigin, Andrew;Dhawan, Vijay;Eidelberg, David
关键词：
为了从特定疾病的大脑网络生成成像生物标志物，我们实现了一个通用工具箱，可以对患者和正常人的大脑图像快速执行基于主成分分析（PCA）的缩放子轮廓建模（SSM）。该 SSMPCA 工具箱可以定义空间协方差模式，其在个体受试者中的表达可以区分患者与对照组或预测行为测量。该技术可能取决于空间标准化算法和大脑成像系统的差异。我们评估了 SSMPCA 在帕金森病 (PD) 患者中产生的特征代谢模式的重现性。我们使用了 PD 患者和正常对照的 [18F] 氟脱氧葡萄糖 PET 扫描。运动相关 (PDRP) 和认知相关 (PDCP) 代谢模式源自使用四个版本的 SPM 软件（spm99、spm2、spm5 和 spm8）进行空间归一化的图像。在多个独立的患者组和对照受试者中比较这些模式和受试者评分之间的差异。在疾病区分和认知相关性方面，这些模式和科目分数在不同的标准化程序中具有高度可重复性。通过多台 PET 扫描仪成像的 PD 患者的受试者评分也具有可比性。我们的研究结果证实，使用四种不同 SPM 平台标准化的图像，大脑网络及其在 PD 中的临床相关性具有非常高的一致性。尽管神经影像学领域的图像预处理软件不断发展，但 SSMPCA 工具箱可以可靠地用于生成疾病特异性成像生物标志物。可以对个体患者的网络表达进行量化，独立于 PET 相机的不同物理特征。
In order to generate imaging biomarkers from disease-specific brain networks, we have implemented a general toolbox to rapidly perform scaled subprofile modeling (SSM) based on principal component analysis (PCA) on brain images of patients and normals. This SSMPCA toolbox can define spatial covariance patterns whose expression in individual subjects can discriminate patients from controls or predict behavioral measures. The technique may depend on differences in spatial normalization algorithms and brain imaging systems. We have evaluated the reproducibility of characteristic metabolic patterns generated by SSMPCA in patients with Parkinson's disease (PD). We used [18F]fluorodeoxyglucose PET scans from PD patients and normal controls. Motor-related (PDRP) and cognition-related (PDCP) metabolic patterns were derived from images spatially normalized using four versions of SPM software (spm99, spm2, spm5 and spm8). Differences between these patterns and subject scores were compared across multiple independent groups of patients and control subjects. These patterns and subject scores were highly reproducible with different normalization programs in terms of disease discrimination and cognitive correlation. Subject scores were also comparable in PD patients imaged across multiple PET scanners. Our findings confirm a very high degree of consistency among brain networks and their clinical correlates in PD using images normalized in four different SPM platforms. SSMPCA toolbox can be used reliably for generating disease-specific imaging biomarkers despite the continued evolution of image preprocessing software in the neuroimaging community. Network expressions can be quantified in individual patients independent of different physical characteristics of PET cameras.
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影响因子：
9.9
作者：
Feigin, A;Fukuda, M;Eidelberg, D
通讯作者：
Eidelberg, D
影响因子：
14.5
作者：
Fukuda, M;Mentis, MJ;Eidelberg, D
通讯作者：
Eidelberg, D
影响因子：
5.7
作者：
Habeck, Christian;Foster, Norman L.;Stern, Yaakov
通讯作者：
Stern, Yaakov
影响因子：
14.5
作者：
Asanuma, Kotaro;Tang, Chengke;Eidelberg, David
通讯作者：
Eidelberg, David
DOI：
10.2967/jnumed.111.089946
发表时间：
2011-06
期刊：
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
影响因子：
--
作者：
Bohnen NI;Koeppe RA;Minoshima S;Giordani B;Albin RL;Frey KA;Kuhl DE
通讯作者：
Kuhl DE